KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning

TL;DR

KagNet is a knowledge-aware graph network that leverages external knowledge graphs and hierarchical attention to improve explainable commonsense reasoning, achieving state-of-the-art results on CommonsenseQA.

Contribution

The paper introduces KagNet, a novel graph network module with hierarchical attention that effectively incorporates external knowledge graphs for commonsense reasoning.

Findings

Achieved state-of-the-art performance on CommonsenseQA dataset.

Demonstrated the effectiveness of hierarchical path-based attention.

Provided transparent and interpretable inference process.

Abstract

Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this paper, we propose a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform explainable inferences. The framework first grounds a question-answer pair from the semantic space to the knowledge-based symbolic space as a schema graph, a related sub-graph of external knowledge graphs. It represents schema graphs with a novel knowledge-aware graph network module named KagNet, and finally scores answers with graph representations. Our model is based on graph convolutional networks and LSTMs, with a hierarchical path-based attention mechanism. The intermediate attention scores make it transparent and interpretable, which thus produce trustworthy inferences. Using ConceptNet as the only external resource for Bert-based models, we achieved state-of-the-art performance on the CommonsenseQA, a large-scale dataset for commonsense reasoning.